The present invention relates to detection of fraudulent clicks in an online pay-per-click environment. Pay-per-click advertising programs offer online advertisers an immediate way to generate qualified website traffic by buying sponsored links on the search result pages of various search engines. However, this method of advertising also is vulnerable to clicks that are not relevant, i.e., are not from traffic genuinely interested in the keyword and/or product or service associated with the keyword. Non-relevant traffic is usually referred to as fraudulent clicks. A common example is a machine, rather than an actual consumer, increasing the traffic for a specific keyword. Fraudulent clicks can take many forms, as discussed herein, each of which can have a significant negative economic effect on the advertiser.
Traditional fraudulent clicks detection services focus on the “pre-click” environment, or the steps that take place to bring a visitor to a site, and examine current impression and click numbers. For example, multiple clicks for a keyword coming from a single IP address or a single website over a short period of time indicates potential fraudulent clicks, as does traffic from a website that is outside of the geographical area in which the advertisement is run. However, pre-click data alone lacks post-click data, or a back-end analysis, such as effect on ROAS (return on advertising spend), which would help catch fraudulent clicks left undetected by this type of pre-click analysis.